Image completion, also known as image inpainting, is a challenging problem in computer graphics and computer vision. Image completion aims at filling in missing pixels in a large unknown region of an image in a visually plausible way. Given an input image with an unknown or missing region, the goal of image completion is to propagate structure and texture information from the known or existing regions I-Ω to Ω, where I is the image region of I. Image completion is inherently an under-constrained problem.
Conventional image inpainting techniques typically work at the pixel level, and have worked well for small gaps, thin structures, and text overlays. However, for larger missing regions or textured regions, these existing systems may generate blurring artifacts. Additionally, conventional image completion systems and techniques typically have difficulty completing images where complex salient structures exist in the missing regions, often resulting in discontinuities in salient structure. Such salient structures include, for example, curves, T-junctions, and X-junctions.